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Fuzzy C-means clustering algorithm based on adaptive neighbors information
Information Sciences | 更新时间:2024-05-06
    • Fuzzy C-means clustering algorithm based on adaptive neighbors information

    • 针对传统模糊C均值(FCM)算法在聚类过程中易受噪声、异常值等因素干扰的问题,研究者在聚类分析领域取得了新进展。他们提出了一种基于自适应近邻信息的模糊C均值聚类算法,通过引入样本点和类中心点的近邻信息,增强了算法对数据结构的感知能力,从而提高了聚类的稳定性和性能。这一创新方法不仅丰富了聚类分析的理论体系,还为实际应用中处理复杂数据提供了有效工具。在基准数据集上的实验结果显示,该算法相较于其他先进聚类算法,性能提升了10%以上。同时,研究者还从参数敏感性、收敛性、消融实验等方面对算法进行了全面评价,进一步验证了其可行性与有效性。这一研究成果对于推动聚类分析领域的发展具有重要意义。
    • Optics and Precision Engineering   Vol. 32, Issue 7, Pages: 1045-1058(2024)
    • DOI:10.37188/OPE.20243207.1045    

      CLC: TP394.1;TH691.9
    • Published:10 April 2024

      Received:28 August 2023

      Revised:11 October 2023

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  • GAO Yunlong,LI Jianpeng,ZHENG Xingshen,et al.Fuzzy C-means clustering algorithm based on adaptive neighbors information[J].Optics and Precision Engineering,2024,32(07):1045-1058. DOI: 10.37188/OPE.20243207.1045.

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